Learning Sparse Networks Using Targeted Dropout

Neural networks are easier to optimise when they have many more weights than are required for modelling the mapping from inputs to outputs. This suggests a two-stage learning procedure that first learns a large net and then prunes away connections or hidden units... (read more)

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METHOD TYPE
Targeted Dropout
Regularization
Dropout
Regularization